Conventionally, in order to recommend a program in the television and radio broadcasting to the user, for example, a program matching information preferred by the user is selected by using program information (or program meta data) such as an EPG (Electronic Program Guide). A method to recommend a program to the user varies in dependence on a method to acquire data preferred by the user. An example of the method to recommend a program to the user is an initial-interest catalog method whereby, first of all, information on interests of the user is initially cataloged in advance and then used as a basis for recommending a program to the user. Another example of the method to recommend a program to the user is a viewing-history utilization method to recommend a program to the user by using a history of programs viewed by the user in the past. A further example of the method to recommend a program to the user is a collaborative filtering method to recommend a program to the user by using viewing histories of other users.
In the initial-interest catalog method, first of all, the user is requested to catalog information such as a category of favorite programs, a preferred genre and the names of favorite talents in advance. Examples of the favorite-programs category are a drama and a variety program. Examples of the preferred genre are a mystery and a comedy. Then, the cataloged information is used as a keyword for recognizing program meta data matching the interest of the user. Finally, the name of a program to be recommended to the user is acquired.
In the viewing-history utilization method, every time the user views a program, the meta data of the viewed program is stored and, as past meta data has been stored to a certain degree, the stored meta data is analyzed to obtain information such as a category of favorite programs, a preferred genre and the names of favorite talents. Then, the obtained information is used as a keyword for recognizing program meta data matching the favorite of the user. Finally, the name of a program to be recommended to the user is acquired.
In an apparatus such as a recording apparatus employing an HDD (Hard Disk Drive), for example, information on favorites is acquired not only on the basis of a viewing history, but also in some cases on the basis of information on a history of recording reservations or a history of user operations such as recording operations. In such cases, it is possible to distinguish a program accidentally viewed by the user from a program intentionally viewed by the user as a program of interest to the user so that information reflecting better favorites of the user can be acquired. A program accidentally viewed by the user is a program viewed by the user as a program presented by a television receiver (or a radio) in a turned-on state not set by the user because the user has a particular interest in the program and views the program.
In the collaborative filtering method, given a first user, first of all, an operation is carried out to search for a second user with a viewing/operation history matching the viewing/operation history of the first user and the viewing/operation history of the second user is acquired. Then, a program is selected among programs viewed by the second user as a program not viewed yet by the first user and recommended to the first user.
In addition, as disclosed in Japanese Patent Laid-open No. 2001-160955, for example, there has been proposed a technology whereby an n-dimensional attribute vector is added in advance to a broadcast program as attributes of the program. Then, a selection vector is compared with the attribute vector to select a program to be recorded or a program to be reproduced. The selection vector is a vector generated from data initially recorded by the user and average values of attributes represented by attribute vectors of programs reproduced by the user or programs with reserved recording.
If a program is selected by adoption of the initial-interest catalog method, however, the selected program represents only a specific interest, which was owned by the user at the time the user initially cataloged information. In addition, in order to record details of the information, the user needs to carry out complicated information-cataloging operations. Thus, in order to simplify the operations to catalog the information to be recorded in the initial setting process, the number of pieces of information to be recorded needs to be reduced. In consequence, only a program selected on the basis of rough information recorded by the user can be recommended. As a result, the degree of precision to select a program matching the favorite of the user is low.
In the other methods such as the viewing-history utilization method, on the other hand, a recommended program is selected by simply using a sum or average value of pieces of meta data, which have been collected on the basis of a viewing history of the user. Thus, if the history is not stored to a certain degree in this case, it is not possible to recommend a program correctly matching the favorite of the user. In addition, in the case of the viewing-history utilization method, correlations among pieces of meta data are insensitive, making sufficient personalization impossible. On the top of that, if histories are piled up, in some cases, biases may be developed in weights due to history items overlapping each other with ease and history items each having an element spreading with ease as a history. History items overlapping each other with ease are items easily detected as a favorite of the user. An example of the history items overlapping each other with ease is a genre. On the other hand, a history item having an element spreading with ease as a history is an item difficult to detect as a favorite of the user. An example of the history item having an element spreading with ease as a history is a performer.
To put it concretely, let us assume for example that the user is a fan of commentator A. Thus, the user enjoys viewing a live coverage of a baseball game by team B with comments made by commentator A. In this case, pieces of information of “a live coverage of a baseball game”, which is a genre, overlap each other with ease as a history. That is to say, the information of “a live coverage of a baseball game” is detected easily as a favorite of the user. However, pieces of information of “commentator A”, which is a performer, hardly overlap each other. That is to say, information of “commentator A” is hardly detected as a favorite of the user. Thus, there is a case in which a live coverage of a baseball game by team B with comments made by another commentator is recommended while a variety program in which commentator A performs is not recommended.
In addition, as disclosed in Japanese Patent Laid-open No. 2001-160955, an attribute vector is added in advance to a broadcast program. Then, a selection vector is compared with the attribute vector to select a program to be recorded or a program to be reproduced. The selection vector is a vector generated from data initially recorded by the user and average values of attributes represented by attribute vectors of programs reproduced by the user or programs with reserved recording. Also in this case, since an operation history of the user is used, in some cases, biases may be developed in weights due to history items overlapping each other with ease and history items each having an element, such as a performer, spreading with ease as a history.
Let us assume for example that the user enjoys dramas and only variety programs of comedian A who does not perform in a drama. In addition, let us assume that the user views such variety programs and dramas at a ratio of 2:8. In a selection vector generated for such a user, pieces of information of “performer B” performing frequently in dramas overlap each other as a history rather than comedian A who hardly performs in a drama in spite of the fact that performer B is not specially a favorite star of the user. Thus, a documentary program in which “performer B” performing frequently in dramas performs is recommended, taking precedence of variety programs having comedian A as a performer.
In addition, an item of importance to selection of a program varies from user to user. For example, a performer is of importance to a certain user while the substance of a program is of importance to another user. Nevertheless, since all items are operated in the same way, a favorite unique to a user may not be reflected in a recommended program in some cases.
On the top of that, since what is utilized in the collaborative filtering method is the favorite of another user, it is difficult to extract information representing the favorite of each user in detail.